Title: Document classification using deep neural network with different word embedding techniques

Authors: Preeti Kathiria; Usha Patel; Nishant Kansara

Addresses: Department of Computer Science and Engineering, Institute of Technology, Nirma University, India ' Department of Computer Science and Engineering, Institute of Technology, Nirma University, India ' Department of Computer Science and Engineering, Institute of Technology, Nirma University, India

Abstract: Document classification has played a major role in many fields like information retrieval, data mining, etc. where machine learning and deep learning models can be applied. But, before applying any model for classification, textual data must be converted into a numerical measure, where word embedding can help. The selection of appropriate word embedding techniques plays a vital role in classification. So, we analysed the classification performance by widely used deep learning models long short-term memory (LSTM) and convolution neural network (CNN) with various word embedding techniques on five benchmark datasets. The pre-processed dataset is converted into vector representation using a word embedding techniques TF-IDF, Word2Vec, and Doc2Vec. The output is given to the LSTM and CNN classifier and documents are classified as per their context. The CNN classifier with Doc2Vec word embedding technique achieves almost 12% more accuracy as compared to other word embedding techniques on all the datasets.

Keywords: document classification; word embedding techniques; deep neural network; TF-IDF; Word2Vec; Doc2Vec.

DOI: 10.1504/IJWET.2022.125654

International Journal of Web Engineering and Technology, 2022 Vol.17 No.2, pp.203 - 222

Received: 01 Dec 2021
Received in revised form: 25 Jun 2022
Accepted: 27 Jun 2022
Published online: 19 Sep 2022 *

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